Brands have never known more about their customers, and shopping has never felt more broken. Shoppers still see ads for products they already bought. They still get recommendations that ignore where they are and what they need. The data is there. The experience it should be producing is not.

"Consumers are expecting character," said Keith Lehmann, global marketing director of digital commerce and retail media at Colgate-Palmolive, on a recent episode of "Reimagining Retail."Being mindful and respectful of time as the biggest currency. Don't waste it."

The disconnect is between capability and execution, and it costs retailers money and customer trust.

The discipline problem

The tools to create seamless customer experiences already exist. Brands can track purchases, remove buyers from ad rotations, and serve personalized content across channels. Yet consumers see the same ads dozens of times, even after buying.

"The technology is 100% there," Lehmann said. The real issue is organizational: "A lot of it's volume. A lot of it is just losing track of all of the campaigns that you have available, and some of it is disorganization or just not having the data connected the correct way."

Volume without coordination burns money and goodwill at the same time. One pharmaceutical brand served Lehmann 24 to 25 ads in a single viewing session, he said, which points to poor campaign management and no frequency capping.

The gap between capability and execution represents a fundamental failure of campaign discipline. Brands need regular audits to ensure purchased customers are removed from retargeting campaigns and that different channels communicate with each other about customer behavior.

Data silos create customer friction

Every new shopping channel hands a little more control over customer data and narrative to the platform running it. Brands are left assembling the picture from fragments.

"As the channels proliferate, brands are kind of seeping, losing control, or giving up control to these platforms," said our analyst Sky Canaves. "And if they don't get what they need, they have to piece it together, and that's where you have more third parties involved."

Age is the handicap here. Companies that have operated for decades keep data in multiple systems that don't talk to each other, sometimes in different languages across global operations.

Smaller challenger brands often use data more effectively because they're built on modern data foundations from the start. "Their full business model is built in the 2020 era, and they know how to start from the ground up," Lehmann noted.

Established brands can't tackle every data challenge simultaneously. The solution is prioritization: Focus on connecting data that directly improves customer experience rather than attempting comprehensive system overhauls that won't be ready until 2035.

Retailer-brand partnerships need stronger alignment

Closer collaboration between retailers and brands would fix many of these disconnects. Retailers possess sophisticated recommendation engines and real-time shopping data, but brands often lack visibility into how retail platforms actually work.

"It would be amazing for retailers to pull up and say, 'Hey, here's new capabilities we're starting to get invented, and created, and connected,'" Lehmann said. "'Here's how our technology works, and here's how it's intended, and here's how it's designed, and here's maybe where you should make a few adjustments.'"

That education divide leaves brands guessing at why customers see certain ads or how to optimize campaigns within retail media networks (RMNs).

AI will amplify both strengths and weaknesses

AI can connect data silos horizontally across an organization, enriching vertical data streams with more precise consumer information. It will also expose every weakness in the foundation underneath.

"Having that proper data foundation becomes absolutely essential, and this is where it does get exposed if it's not there or if it's so disconnected or disorganized," Canaves said. "It's still very much the case that garbage in is garbage out with AI."

AI is a directional shift, not a temporary trend, which is why the stakes are higher. Competitors are working on their data foundations now.

AI requires human discernment and oversight. "AI lacks discernment," Lehmann said. "It can't make those critical decisions." But it can serve as a catalyst, forcing organizations from CEO to intern to examine their business operations, identify missing information, and build better data foundations.

Listen to the full episode.